搜藏一个较全的数据集目录
这个页面比较详细:http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
此外cvpapers的页面一直更新:http://www.cvpapers.com/datasets.html
室内RGB_D场景分割:https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
Microsoft COCO - Common Objects in Context (Tsung-Yi Lin et al)
COCO
COCO(Common Objects in Context)是一个新的图像识别、分割和图像语义数据集,它有如下特点: 1)Object segmentation2)Recognition in Context3)Multiple objects per image4)More than 300,000 images5)More than 2 Million instances6)80 object categories7)5 captions per image8)Keypoints on 100,000 people
COCO数据集由微软赞助,其对于图像的标注信息不仅有类别、位置信息,还有对图像的语义文本描述,
COCO数据集的开源使得近两三年来图像分割语义理解取得了巨大的进展,也几乎成为了图像语义理解
算法性能评价的“标准”数据集。
Segmentation (General)
- : Shadow Detection/Texture Segmentation Computer Vision Dataset- Video based sequences for shadow detection/suppression, with ground truth (Newey, C., Jones, O., & Dee, H. M.)
- Aberystwyth Leaf Evaluation Dataset- Timelapse plant images with hand marked up leaf-level segmentations for some time steps, and biological data from plant sacrifice. (Bell, Jonathan; Dee, Hannah M.)
- Alpert et al. Segmentation evaluation database (Sharon Alpert, Meirav Galun, Ronen Basri, Achi Brandt)
- BMC (Background Model Challenge)- A dataset for comparing background subtraction algorithms, comp=osed of real and synthetic videos(Antoine)
- Berkeley Segmentation Dataset and Benchmark (David Martin and Charless Fowlkes)
- CAD 120 affordance dataset - Pixelwise affordance annotation in human context (Sawatzky, Srikantha, Gall)
- CTU Color and Depth Image Dataset of Spread Garments- Images of spread garments with annotated corners.(Wagner, L., Krejov D., and Smutn V. (Czech Technical University in Prague))
- CTU Garment Folding Photo Dataset- Color and depth images from various stages of garment folding.(Sushkov R., Melkumov I., Smutn y V. (Czech Technical University in Prague))
- DeformIt 2.0- Image Data Augmentation Tool: Simulate novel images with ground truth segmentations from a single image-segmentation pair (Brian Booth and Ghassan Hamarneh)
- GrabCut Image database (C. Rother, V. Kolmogorov, A. Blake, M. Brown)
- ICDAR'15 Smartphone document capture and OCR competition - challenge 1 - videos of documents filmed by a user with a smartphone to simulate mobile document capture, and ground truth coordinates of the document corners to detect. (Burie, Chazalon, Coustaty, Eskenazi, Luqman, Mehri, Nayef, Ogier, Prum and Rusinol)
- Intrinsic Images in the Wild (IIW)- Intrinsic Images in the Wild, is a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes (Sean Bell, Kavita Bala, Noah Snavely)
- LabelMe images database and online annotation tool (Bryan Russell, Antonio Torralba, Kevin Murphy, William Freeman)
- LITS Liver Tumor Segmentation - 130 3D CT scans with segmentations of the liver and liver tumor. Public benchmark with leaderboard at Codalab.org (Patrick Christ)
- Materials in Context (MINC)- The Materials in Context Database (MINC) builds on OpenSurfaces, but includes millions of point annotations of material labels. (Sean Bell, Paul Upchurch, Noah Snavely, Kavita Bala)
- OpenSurfaces- OpenSurfaces consists of tens of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated with material parameters, texture information, and contextual information . (Kavita Bala et al.)
- Osnabrück gaze tracking data - 318 video sequences from several different gaze tracking data sets with polygon based object annotation (Schöning, Faion, Heidemann, Krumnack, Gert, Açik, Kietzmann, Heidemann & König)
- PetroSurf3D- 26 high resolution (sub-millimeter accuracy) 3D scans of rock art with pixelwise labeling of petroglyphs for segmentation(Poier, Seidl, Zeppelzauer, Reinbacher, Schaich, Bellandi, Marretta, Bischof)
- SYNTHIA - Large set (~half million) of virtual-world images for training autonomous cars to see. (ADAS Group at Computer Vision Center)
- Stony Brook University Shadow Dataset (SBU-Shadow5k)- Large scale shadow detection dataset from a wide variety of scenes and photo types, with human annotations (Tomas F.Y. Vicente, Le Hou, Chen-Ping Yu, Minh Hoai, Dimitris Samaras)
Urban Datasets
- Barcelona - 15,150 images, urban views of Barcelona (Tighe and Lazebnik)
- CMP Facade Database- Includes 606 rectified images of facades from various places with 12 architectural classes annotated.(Radim Tylecek)
- LM+SUN - 45,676 images, mainly urban or human related scenes (Tighe and Lazebnik)
- MIT CBCL StreetScenes Challenge Framework: (Stan Bileschi)
- Queen Mary Multi-Camera Distributed Traffic Scenes Dataset (QMDTS)- The QMDTS is collected from urban surveillance environment for the study of surveillance behaviours in distributed scenes.(Dr. Xun Xu. Prof. Shaogang Gong and Dr. Timothy Hospedales)
- Robust Global Translations with 1DSfMthe numerical data describing global structure from motion problems for each dataset (Kyle Wilson and Noah Snavely)
- Sift Flow (also known as LabelMe Outdoor, LMO) - 2688 images, mainly outdoor natural and urban (Tighe and Lazebnik)
- Street-View Change Detection with Deconvolutional Networks- Database with aligned image pairs from street-view imagery with structural,lighting, weather and seasonal changes.(Pablo F. Alcantarilla, Simon Stent, German Ros, Roberto Arroyo and Riccardo Gherardi)
- SydneyHouse - Streetview house images with accurate 3D house shape, facade object label, dense point correspondence, and annotation toolbox.(Hang Chu, Shenlong Wang, Raquel Urtasun,Sanja Fidler)
- Traffic Signs Dataset- recording sequences from over 350 km of Swedish highways and city roads (Fredrik Larsson)
转载于:https://www.cnblogs.com/wishchin/p/9199881.html
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